How Pacmed & Eli5 Helped ICU Doctors Make Smarter Discharge Decisions with AI: How Pacmed & Eli5 Helped ICU Doctors Make Smarter Discharge Decisions with AI

Challenge

Determining the optimal moment to discharge a patient from the ICU is a high-stakes, data-driven decision. Each patient generates around 30,000 data points per day, from vital signs to lab results. Discharging too early risks readmission and increased mortality; too late, and patients may suffer unnecessary consequences while blocking critical ICU beds for others in need.

Approach

Pacmed developed a sophisticated algorithm to predict the necessity of continued ICU care, analyzing vast amounts of patient data with advanced statistical techniques. Eli5’s task: build a user-friendly dashboard for Pacmed Critical that would translate this complex analysis into clear, actionable information for ICU doctors.

The dashboard visualizes key patient data—length of stay, current status, trends over time, and readiness for discharge—helping doctors make informed decisions. Importantly, the algorithm supports, but never replaces, clinical judgment, offering transparency into the factors influencing each prediction.

Insight

Pacmed Critical draws on 14 years of data from 16,000 ICU admissions in Amsterdam, using machine learning to identify patterns and predict outcomes. The tool enables doctors to quickly answer critical questions, such as: “What is the chance of re-admission or premature death within seven days if the patient leaves now?” By surfacing insights from similar cases, the software accelerates and improves decision-making.